Abstract

Abstract

Abstract
Selecting the right set of descriptors for the annotation of a specific dataset can be a hard problem in research data management. Considering a dataset in an arbitrary domain, an application profile is complex to build because of the abundance of metadata standards, ontologies and other descriptor sources available for different domains. We propose to partially automate the process of data description by generating application profile recommendations based on a research data asset knowledge base. Our approach builds on existing technologies for exploring linked data and results in a process which can be tightly coupled with the research workflow, giving researchers more control over the description of their data. Preliminary experiments show that we can build on state-of-the-art technologies for search indexes, graph databases and triple stores to explore existing sources of linked data for our profile generation.